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13 papers · ranked by Valyu relevance

01
BbioRxiv8 Jun 2026
The ENIGMA MEG Pipeline: Automated cortically localized spectral analysis of multi-site resting state MEG datasets
Allison C Nugent, Anna M Namyst, Frederick W Carver, Paul M Thompson + 1 more
Magnetoencephalography (MEG) is a unique technique in human neuroimaging combining high temporal resolution (millisecond or faster) with moderate spatial resolution (several millimeter). While many software packages for MEG data analysis exist, there is no pipeline developed for the specific purpose of enabling the…
80relevance
02
BbioRxiv3 Jun 2025
*eyeris:* A flexible, extensible, and reproducible pupillometry preprocessing framework in R
Shawn T. Schwartz, Haopei Yang, Alice M. Xue, Mingjian He
Pupillometry provides a non-invasive window into the mind and brain, particularly as a psychophysiological readout of autonomic and cognitive processes like arousal, attention, stress, and emotional states. Pupillometry research lacks a robust, standardized framework for data preprocessing, whereas in functional…
70relevance
03
BbioRxiv8 Jun 2026
Brainana: an end-to-end preprocessing framework for macaque neuroimaging
Xingyu Liu, Yijun Zhang, Zi Yin, Zonglei Zhen + 1 more
Macaque MRI bridges non-invasive systems neuroscience with cellular and circuit-level mechanisms, but preprocessing tools remain difficult to integrate and deploy reproducibly. We present Brainana, an automated, BIDS-compatible preprocessing and visualization framework for macaque neuroimaging. Brainana integrates…
70relevance
04
BbioRxiv10 May 2023
Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models^⋆^
Lara Dular, Franjo Pernuš, Žiga Špiclin
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years…
69relevance
05
BbioRxiv27 Mar 2026
TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems
Yanfan Zhu, Marilyn M. Lionts, Ezekiel J. Haugen, Alec B. Walter + 10 more
Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful…
68relevance
06
BbioRxiv25 Feb 2022
Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis
Sebastiaan Mathôt, Ana Vilotijević
Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes such as attention, mental effort, working memory, and many others. Currently, there is no commonly agreed-upon methodology for conducting cognitive-pupillometry experiments, and approaches vary widely between research groups and…
67relevance
07
BbioRxiv1 May 2024
Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration
Nadine S. J. Jacobsen, Daniel Kristanto, Suong Welp, Yusuf Cosku Inceler + 1 more
Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where…
67relevance
08
BbioRxiv22 Sep 2023
Connectome Operations For FSL ExEcution (COFFEE): a turnkey pipeline for preprocessing of fMRI data
Mark M. McAvoy, Lei Liu, Ruiwen Zhou, Benjamin A. Philip
Numerous methods exist to analyze functional MRI (fMRI) data, but no software currently exists to integrate the commonly-used FSL statistical analysis software with alternative preprocessing methods from the Human Connectome Project. Here we developed the Connectome Operations For FSL ExEcution (COFFEE) pipeline to…
66relevance
09
BbioRxiv22 Jul 2025
DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow
Jiayuan Ding, Zhongyu Xing, Yixin Wang, Renming Liu + 9 more
Preprocessing is a critical step in single-cell data analysis, yet current practices remain largely a black-box, trial-and-error process driven by user intuition, legacy defaults, and ad hoc heuristics. The optimal combination of steps such as normalization, gene selection, and dimensionality reduction varies across…
66relevance
10
BbioRxiv22 Jun 2023
*Is* EEG is better left alone?
Alain de Cheveigné
A recent article (Delorme, A., 2023, EEG is better left alone. Scientific Reports, 13, 2372. https://doi.org/10.1038/s41598-023-27528-0) proposed a metric to determine the benefit of applying pre-processing methods to EEG data. Using that metric, it concluded that most pre-processing methods do not improve data…
65relevance
11
BbioRxiv3 Jan 2023
RNA-seq preprocessing and sample size considerations for gene network inference
Gökmen Altay, Jose Zapardiel-Gonzalo, Bjoern Peters
Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are…
64relevance
12
BbioRxiv20 Apr 2025
Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction
R. Austin Benn, Ting Xu, Rogier B. Mars, Magdalena Boch + 11 more
Cortical surface reconstruction has changed how we study brain morphology and geometry. However, extending these methods to non-human species has been limited by the lack of standardized pipelines, anatomical templates, and variability in imaging protocols. To address these challenges, we present Precon_all, an…
63relevance
13
BbioRxiv30 Sep 2022
Predicting nutrient profiles in food after processing
Tarini Naravane, Ilias Tagkopoulos
The future of personalized health relies on knowledge of dietary composition. The current analytical methods are impractical to scale up, and the computational methods are inadequate. We propose machine learning models to predict the nutritional profiles of cooked foods given the raw food composition and cooking…
52relevance

Results for additional preprocessing

The ENIGMA MEG Pipeline: Automated cortically localized spectral analysis of multi-site resting state MEG datasets

eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Brainana: an end-to-end preprocessing framework for macaque neuroimaging

Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models^⋆^

TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Connectome Operations For FSL ExEcution (COFFEE): a turnkey pipeline for preprocessing of fMRI data

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

Is EEG is better left alone?

RNA-seq preprocessing and sample size considerations for gene network inference

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Predicting nutrient profiles in food after processing

The ENIGMA MEG Pipeline: Automated cortically localized spectral analysis of multi-site resting state MEG datasets

eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Brainana: an end-to-end preprocessing framework for macaque neuroimaging

Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models^⋆^

TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Connectome Operations For FSL ExEcution (COFFEE): a turnkey pipeline for preprocessing of fMRI data

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

Is EEG is better left alone?

RNA-seq preprocessing and sample size considerations for gene network inference

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Predicting nutrient profiles in food after processing

Results for additional preprocessing

The ENIGMA MEG Pipeline: Automated cortically localized spectral analysis of multi-site resting state MEG datasets

*eyeris:* A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Brainana: an end-to-end preprocessing framework for macaque neuroimaging

Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models^⋆^

TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Connectome Operations For FSL ExEcution (COFFEE): a turnkey pipeline for preprocessing of fMRI data

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

*Is* EEG is better left alone?

RNA-seq preprocessing and sample size considerations for gene network inference

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Predicting nutrient profiles in food after processing

The ENIGMA MEG Pipeline: Automated cortically localized spectral analysis of multi-site resting state MEG datasets

*eyeris:* A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Brainana: an end-to-end preprocessing framework for macaque neuroimaging

Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models^⋆^

TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Connectome Operations For FSL ExEcution (COFFEE): a turnkey pipeline for preprocessing of fMRI data

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

*Is* EEG is better left alone?

RNA-seq preprocessing and sample size considerations for gene network inference

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Predicting nutrient profiles in food after processing

eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Is EEG is better left alone?

eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R

Is EEG is better left alone?